Methods and devices for shortening the convergence time of blind, adaptive equalizers

ABSTRACT

The convergence time of a blind, adaptive equalizer is shortened by using a tracking generator. The tracking generator comprises a smoothing filter which receives and smoothes a tap coefficient error estimate derived from an output data stream. Thereafter, a fraction of the smoothed estimate is generated. It is the use of this function of the smoothes estimate which allows the convergence time to be shortened.

RELATED APPLICATION

The present invention is a continuation of U.S. Ser. No. 09/902,160filed Jul. 11, 2001 now U.S. Pat. No. 7,068,736 which is incorporated byreference herein in its entirety.

BACKGROUND OF THE INVENTION

Data sent through a channel is subject to linear distortion as well asother impairments. An “equalizer” is a device which is used to remove,or reduce (hereafter collectively referred to as “compensate”) lineardistortion and is commonly made a part of a receiver.

One type of equalizer is called an “adaptive” equalizer. Adaptiveequalizers comprise “tap coefficients” that are continuously adjusted,with the goal of producing an error free (i.e., distortion-free) outputsignal.

Adaptive equalizers typically operate in various modes, two of which area “decision directed” and “blind” mode.

Decision directed, adaptive equalizers make use of an “ideal” referencesignal. For example, a pseudo-random sequence may be used as an idealreference. Sometimes, however, it is not practical to use an idealreference. Blind, adaptive equalizers may then be used. Such equalizersuse a “fixed” reference. The value of the fixed reference always remainsthe same as compared with an ideal reference, whose value may changerandomly. Both decision directed and blind adaptive equalizers require a“start-up time”. It has been known for some time that adaptiveequalizers require a certain start-up time before they can be used toaccurately receive and recreate data. This time is called a “convergencetime”. Typically, decision directed, adaptive equalizers have shorterconvergence times than blind adaptive equalizers. This is due to thefact that a decision directed, adaptive equalizer's tap coefficients maybe adjusted in large increments without creating a large deviation froma mean (i.e., statistical mean) error. In contrast, the tap coefficientsof a blind, adaptive equalizer must be incremented in very small amountsresulting in equalizers which take 100 times as long (i.e., two ordersof magnitude longer) to reach convergence than decision directed,adaptive equalizers. Efforts to date to increment tap coefficients of ablind, adaptive equalizer in relatively large increments have not provensuccessful. Existing techniques inherently generate too much noise,which prevents an equalizer from reaching convergence.

It is believed that the convergence time of existing blind, adaptiveequalizers takes longer than necessary.

It is therefore a desire of the present invention to provide for methodsand devices which shorten the time it takes for blind, adaptiveequalizers to reach convergence.

SUMMARY OF THE INVENTION

In accordance with the present invention, methods and devices areprovided for shortening the convergence time of blind, adaptiveequalizers. As envisioned by the present invention, an equalizercomprises a tracking generator. During a start-up period, the trackinggenerator receives and smoothes tap coefficient error estimates derivedfrom an output data stream. The generator then generates a fractionalerror from the smoothed, tap coefficient error estimates. Even thoughonly a fraction of the smoothed error estimates are used, theadjustments made to tap coefficients are larger than previously thoughtpossible.

These larger adjustments shorten the time needed by a blind, adaptiveequalizer to reach convergence as compared to existing equalizers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a simplified block diagram of a blind, adaptive equalizeraccording to one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention envisions blind, adaptive equalizers which areadapted to reach convergence using relatively large adjustments to tapcoefficients. This equates to reducing the time (e.g., by at least oneorder of magnitude) it takes for a blind, adaptive equalizer (hereafter“equalizer” unless otherwise noted) to reach convergence compared toexisting techniques.

Referring now to FIG. 1, there is shown a simplified, high-level blockdiagram of a blind, adaptive equalizer 1 according to one embodiment ofthe present invention. Equalizer 1 may comprise one or more integratedcircuits, discrete devices, or programmed media (e.g., digital signalprocessor, emulator or the like adapted to store one or more softwareprograms) adapted to carry out the features and functions of the presentinvention.

In an illustrative embodiment of the present invention, equalizer 1comprises: data register or sampling unit 2, output data stream orsignal generator 3, equalizer error generator or unit 4, leveladjustment unit 5, coefficient error estimator or unit 6, trackinggenerator 7 and coefficient generator or unit 10. In one embodiment ofthe present invention, the tracking generator 7 comprises an exponentialsmoothing unit or filter 9 and tracking adjustment units 8 a, 8 b(hereinafter collectively referred to as “tracking adjustment unit”).The smoothing filter 9 in turn comprises a series of adders andmultipliers (symbolized by the circles enclosing “+” and “×” in FIG. 1)12 and an error storage unit or tracking memory 11 (identified by theletters “TR” in FIG. 1). The operation of equalizer 1 will now bedescribed in more detail.

At preselected clock intervals “n”, equalizer 1 is adapted to generate areceived output data stream or signal y_(n) via pathway 100 from aninput data stream or signal x by applying tap coefficients c₀, c₁,c_(2 . . .) c_(m-1), (collectively c_(k,n,) and indicated by coefficientstorage unit 13 in FIG. 1) received from coefficient generator 10 tosampled outputs x₀, x₁, x_(2 . . .) x_(M-1) (collectively, x_(n)) where“M” corresponds to the total number of “taps”, or sampling stages ofdata register 2.

At the beginning of a start-up period, the output data stream y_(n) willnot be an accurate copy of the original transmitted data stream. Saidanother way it is the output data stream, y_(n), which must convergebefore the equalizer 1 can be relied upon to correctly recover the inputdata stream x.

In an illustrative embodiment of the present invention, equalizer 1reaches convergence as follows.

Upon receiving data samples x_(n) from data register 2 and coefficientfactors c_(k,n) from coefficient generator 10, the signal generator 3generates the “dot” product of each output x_(n) and coefficient c_(n),(symbolized by the circle enclosing a “.” in FIG. 1), to sum all ofthese data signals and to generate output signal y_(n).

Assuming at this point that the output signal y_(n), remains aninaccurate copy of the input signal x, (i.e., data stream) errorspresent in tap coefficients 13 must be reduced. In an illustrativeembodiment of the present invention, the generator 3 forwards the outputsignal y_(n) to equalizer error generator 4 at the end of each “symbol”period (e.g., typically equal to 4 sampling periods). Generator 4thereafter generates an equalizer output error, e_(n), by subtracting a“blind reference” signal “BR” derived from a reference z supplied by ablind reference generator (“BRG”) 900. The error e_(n) is an indicationof how close or how far from convergence the output signal or datastream y_(n) is at the end of a given symbol period.

In one embodiment of the present invention, the reference signal BR maycomprise z times the SGN (Y).

Upon generating the error e_(n), the generator 4 sends it to the leveladjustment unit 5. In one embodiment of the present invention, theadjustment unit 5 multiplies the error with an adjustment factor, α₂.Generally, the adjustment factor α₂ is derived from the inverse of theaverage power of all of the signal samples, x_(n).

After combining the error with the adjustment factor α₂, the unit 5generates an “adjusted” equalizer error (“adjusted error”) and forwardsthe adjusted error to the coefficient error estimator 6. The estimator 6multiplies the adjusted error with each of the data samples x_(n) inorder to generate and output one or more tap coefficient error estimates(“estimated error”). This error is then sent to the tracking generator7.

It should be understood that though only a single output from the errorestimator 6 and single input into the tracking generator 7 is shown inFIG. 7, the invention is not so limited. In fact, the estimator 6 willtypically output a plurality of outputs (i.e., “N outputs”). Similarly,tracking generator 7 typically comprises a plurality of trackinggenerators.

Existing equalizers (e.g., decision-directed equalizers) would attemptto use this estimated error to generate new tap coefficients c_(n). Thatis, this error would typically be sent to the coefficient generator 10which would then generate new coefficients. These coefficients wouldsubsequently be used to generate a new output signal, y_(n+1), In thecase of a blind, adaptive equalizer the estimated error, however, is toolarge. Said another way, the estimated error output by unit 6 comprisesa large noise component. Generating new tap coefficients using such a“noisy” error will fail to reduce the standard deviation of the error toa small enough value so that the equalizer 1 can converge. Instead, anoisy error only generates a noisy output signal y_(n).

Realizing this, the present invention envisions an equalizer 1 whichcomprises a tracking generator 7 adapted to generate and output afraction of the error estimate (hereafter referred to as a “fractionalerror”).

In an illustrative embodiment of the present invention, the generator 7comprises smoothing filter 9 and tracking unit 8 a, 8 b. The smoothingfilter 9 is adapted to receive the error estimate and to generate an“averaged, smoothed” error (hereafter “smoothed error”). Morespecifically, adders and multipliers 12 are adapted to generate thesmoothed error by processing the error estimate in combination with asmoothing factor, α₀. Thereafter, the memory 11 is adapted to store thesmoothed error.

Once the smoothed error is stored, it is sent to tracking adjustmentunit 8 a, 8 b. It is this unit which generates the fractional error. Inan illustrative embodiment of the present invention, the unit 8 b isadapted to receive the smoothed error via pathway 200 and a coefficientadjustment factor α₁ via pathway 300. The unit 8 b is further adapted tomultiply these two values together ( . . . for example . . . ) togenerate a fractional error. It should be understood that, bycontrolling the value of the coefficient adjustment factor α₁, the valueof the fractional error may also be controlled. That is, the larger thevalue of α₁, the greater the value of the fractional error andvice-versa. The coefficient adjustment factor may be any value desired,such as 2⁻⁸ or 1/256, for example.

Once the fractional error has been generated, the unit 8 b outputs it tothe coefficient generator 10 via pathway 500 and to unit 8 a via pathway600. Unit 8 a subtracts the fractional error from the stored, smoothederror received via pathway 400 in order to generate a new, smoothederror or reduced error. This reduced error is sent to memory 11 viapathway 700. During the next sampling period, the tracking generator 7will generate a new or “next” fractional error based on this reducederror. Thus, it can be said that the generator 7 is adapted to subtractan error value which is equal to the value output to coefficientgenerator 10. Because the generator 7 reduces the smoothed error eachtime a fractional error is output, the generator 7 can be said to“track” the smoothed error.

Because only a fraction of the smoothed, coefficient error estimate issent to the coefficient generator 10, only a fraction of the noise ispassed on as well. In sum, the new coefficients generated by generator10 are less noisy than would ordinarily be expected.

Completing the cycle, upon receiving the fractional error via pathway500, the coefficient generator 10 generates new, adjusted tapcoefficients, c_(n+1) by combining (e.g., multiplying) the existingcoefficients by the fractional error.

During the next sampling period, the generator 10 (or storage unit 13)outputs new, adjusted tap coefficients to output signal generator 3.Thereafter, generator 3 combines the adjusted tap coefficients with thenext signal samples, X_(n+1) . Eventually, the equalizer is adapted tooutput a next output signal, y_(n+1) after the next symbol period. Fromhere, the process continues as described above (i.e., new fractionalerrors are generated which generate new, adjusted tap coefficients) foras many cycles or iterations until the output y_(n) reaches convergence.There are any number of ways that a convergence point or range may bedetected. For example, one way is to measure the equalizer output errore_(n), process it (e.g., square its value and then apply the new valueto an exponential smoothing filter) and then measure whether the valueof e_(n) falls below a threshold. If so, the equalizer output y_(n) canbe said to have converged.

The discussion above includes examples or embodiments which may be usedto carry out the features and functions of the present invention. Othersmay be envisioned. For example, after the equalizer 1 reachesconvergence, it may be adapted to operate as a decision-directedequalizer by forwarding the adjusted equalizer error directly from unit5 to coefficient generator 10. In addition, though shown as separateunits. some or all of units 1-13 maybe us combined into fewer units orfurther broken down into additional units.

It should be understood that although only a fraction of each smoothedcoefficient error is used, each fraction results in the generation ofnew coefficients in increments which are at least one order of magnitudelarger than generated by existing blind, adaptive equalizers. Becauserelatively larger increments are used, equalizers envisioned by thepresent invention reach convergence faster than existing blind, adaptiveequalizers.

It should be understood that variations may be made by those skilled inthe art without departing from the spirit and the scope of the presentinvention as defined by the claims which follow.

1. An adaptive equalizer comprising: a signal generator for generatingan output data stream from input data samples in combination withadjusted tap coefficients; a smoothing filter for generating a smoothederror from a tap coefficient error estimate, wherein the tap coefficienterror estimate is based on multiplying a data sample of the input datastream with an adjusted equalizer error based on a blind referencesignal; a tracking adjustment unit for generating a fractional errorfrom the smoothed error; and an adaptive coefficient generator forgenerating adjusted tap coefficients based on the fractional error. 2.The adaptive equalizer of claim 1 wherein the adjusted equalizer erroris an indication of convergence of the output data stream to the inputdata stream.
 3. The adaptive equalizer of claim 1 further comprising: ablind reference generator for generating the blind reference signal; anequalizer error generator for generating an equalizer error by combiningthe blind reference signal with the output data stream; and a leveladjustment unit for generating the adjusted equalizer error by combiningthe equalizer error with an adjustment factor.
 4. The adaptive equalizerof claim 1 wherein the smoothed error is generated from the tapcoefficient error estimate in combination with a smoothing factor. 5.The adaptive equalizer of claim 1 wherein the fractional error isgenerated from the smoothed error in combination with a coefficientadjustment factor.
 6. The adaptive equalizer of claim 5 wherein thecoefficient adjustment factor is a controlled value that may be adjustedto control the fractional error.
 7. The adaptive equalizer of claim 1further comprising: a second tracking adjustment unit for subtractingthe fractional error from a stored smoothed error to generate a reducederror value which is used to reduce the fractional error.
 8. Theadaptive equalizer of claim 1 wherein after the adaptive equalizerreaches convergence, the adaptive coefficient generator operates as adecision-directed equalizer and generates adjusted tap coefficientsdirectly based on the adjusted equalizer error.
 9. A method of adjustingtap coefficients comprising: adjusting an equalizer error based on ablind reference signal to generate an adjusted equalizer error;multiplying a data sample of an input data stream with the adjustedequalizer error to generate a tap coefficient error estimate; generatinga smoothed error from the tap coefficient error estimate; generating afractional error from the smoothed error; and adjusting the tapcoefficients based on the fractional error.
 10. The method of claim 9further comprising: adjusting the tap coefficients until the equalizererror falls below a threshold.
 11. The method of claim 10 furthercomprising: generating the blind reference signal as a function of theoutput data stream and a reference signal; generating the equalizererror by combining the blind reference signal with the output datastream; and adjusting the equalizer error to generate an adjustedequalizer error by multiplying the equalizer error with an adjustmentfactor.
 12. The method of claim 10 further comprising: generatingadjusted tap coefficients directly based on the adjusted equalizererror.
 13. The method of claim 9 wherein the smoothed error is generatedby combining the tap coefficient error estimate with a smoothing factor.14. The method of claim 9 wherein the fractional error is generated bycombining the smoothed error with a coefficient adjustment factor.
 15. Amethod of converging an output data stream comprising: generating anoutput data stream from input data samples in combination with adjustedtap coefficients; adjusting an equalizer error based on a blindreference signal to generate an adjusted equalizer error; multiplying adata sample of the input data samples with the adjusted equalizer errorto generate a tap coefficient error estimate; generating a smoothederror from the tap coefficient error estimate; generating a fractionalerror that tracks the smoothed error; and adjusting tap coefficientsbased on the fractional error.
 16. The method of claim 15 furthercomprising: adjusting the tap coefficients until the equalizer errorfalls below a threshold.
 17. The method of claim 15 further comprising:generating the blind reference signal as a function of the output datastream and a reference signal; generating the equalizer error bycombining the blind reference signal with the output data stream; andadjusting the equalizer error to generate an adjusted equalizer error bymultiplying the equalizer error with an adjustment factor.
 18. Themethod of claim 15 further comprising: processing the equalizer error toproduce a processed equalizer error; and measuring the processedequalizer error to determine whether the processed equalizer error fallsbelow a threshold to indicate convergence has been reached.
 19. Themethod of claim 18 wherein the processing further comprises: squaringthe equalizer error value; and applying the squared equalizer value toan exponential smoothing filter to produce the processed equalizererror.
 20. The method of claim 15 further comprising: multiplying aplurality of data samples of the input data samples with the adjustedequalizer error to generate a plurality of tap coefficient errorestimates; generating a plurality of smoothed errors from the pluralityof tap coefficient error estimates; generating a plurality of fractionalerrors that track the corresponding smoothed error; and adjusting tapcoefficients based on the plurality of fractional errors,whereby theoutput data stream is converged.